The original objective of this work was to assess performance of three survival analysis methods for predicting time to event in heart failure studies. The three methods were Cox regularized regression (Cox-LASSO), generalized additive Cox regression (Cox-GAM), and random survival forest, which provides a popular alternative from the Machine Learning community. The first two methods have the attractive properties of allowing for variable selection (Cox-LASSO) and modeling of non-linear features (Cox-GAM). However, currently there is no method that can incorporate both of these desirable properties. Therefore, we developed a novel hybrid method involving generalized additive Cox regression with additional L1-penalty (Cox-GAMLASSO), which has been submitted to CRAN as an R-package. All models were trained and tested on data from two large studies in the Novartis Heart Failure database that includes both clinical and biomarker data. Model comparisons were made based on both discrimination and calibration performance. We conclude with lessons learned about the advantages and disadvantages of the different Statistical & Machine Learning methods.